Ejercicio_1
JuanRomero
6/6/2021
df_houses_prices = read.csv("house_prices.csv")
df_houses_prices %>% head()## MSSubClass LotFrontage LotShape X1stFlrSF TotalBsmtSF GrLivArea FireplaceQu
## 1 60 65 Reg 856 856 1710 <NA>
## 2 20 80 Reg 1262 1262 1262 TA
## 3 60 68 IR1 920 920 1786 TA
## 4 70 60 IR1 961 756 1717 Gd
## 5 60 84 IR1 1145 1145 2198 TA
## 6 50 85 IR1 796 796 1362 <NA>
## GarageArea Heating ExterQual MasVnrArea BsmtFinSF1 BsmtUnfSF CentralAir
## 1 548 GasA Gd 196 706 150 Y
## 2 460 GasA TA 0 978 284 Y
## 3 608 GasA Gd 162 486 434 Y
## 4 642 GasA TA 0 216 540 Y
## 5 836 GasA Gd 350 655 490 Y
## 6 480 GasA TA 0 732 64 Y
## SalePrice
## 1 208500
## 2 181500
## 3 223500
## 4 140000
## 5 250000
## 6 143000Resumen de datos
df_houses_prices %>% summary() ## MSSubClass LotFrontage LotShape X1stFlrSF TotalBsmtSF
## Min. : 20.0 Min. : 21.00 IR1:484 Min. : 334 Min. : 0.0
## 1st Qu.: 20.0 1st Qu.: 59.00 IR2: 41 1st Qu.: 882 1st Qu.: 795.8
## Median : 50.0 Median : 69.00 IR3: 10 Median :1087 Median : 991.5
## Mean : 56.9 Mean : 70.05 Reg:925 Mean :1163 Mean :1057.4
## 3rd Qu.: 70.0 3rd Qu.: 80.00 3rd Qu.:1391 3rd Qu.:1298.2
## Max. :190.0 Max. :313.00 Max. :4692 Max. :6110.0
## NA's :259
## GrLivArea FireplaceQu GarageArea Heating ExterQual
## Min. : 334 Ex : 24 Min. : 0.0 Floor: 1 Ex: 52
## 1st Qu.:1130 Fa : 33 1st Qu.: 334.5 GasA :1428 Fa: 14
## Median :1464 Gd :380 Median : 480.0 GasW : 18 Gd:488
## Mean :1515 Po : 20 Mean : 473.0 Grav : 7 TA:906
## 3rd Qu.:1777 TA :313 3rd Qu.: 576.0 OthW : 2
## Max. :5642 NA's:690 Max. :1418.0 Wall : 4
##
## MasVnrArea BsmtFinSF1 BsmtUnfSF CentralAir SalePrice
## Min. : 0.0 Min. : 0.0 Min. : 0.0 N: 95 Min. : 34900
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 223.0 Y:1365 1st Qu.:129975
## Median : 0.0 Median : 383.5 Median : 477.5 Median :163000
## Mean : 103.7 Mean : 443.6 Mean : 567.2 Mean :180921
## 3rd Qu.: 166.0 3rd Qu.: 712.2 3rd Qu.: 808.0 3rd Qu.:214000
## Max. :1600.0 Max. :5644.0 Max. :2336.0 Max. :755000
## NA's :8Analisis de las variables
for (name in colnames(df_houses_prices)){
print(name)
print(df_houses_prices %>% select("var" = name) %>%
ggplot(aes(x=var, y=..density..)) +
geom_density(col="magenta", lwd=1, fill = "cyan" ) +
theme_minimal())
}## [1] "MSSubClass"## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(name)` instead of `name` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## [1] "LotFrontage"## Warning: Removed 259 rows containing non-finite values (stat_density).
## [1] "LotShape"
## [1] "X1stFlrSF"
## [1] "TotalBsmtSF"
## [1] "GrLivArea"
## [1] "FireplaceQu"
## [1] "GarageArea"
## [1] "Heating"## Warning: Groups with fewer than two data points have been dropped.## Warning in max(ids, na.rm = TRUE): ningun argumento finito para max; retornando
## -Inf
## [1] "ExterQual"
## [1] "MasVnrArea"## Warning: Removed 8 rows containing non-finite values (stat_density).
## [1] "BsmtFinSF1"
## [1] "BsmtUnfSF"
## [1] "CentralAir"
## [1] "SalePrice"
for (var_x in colnames(df_houses_prices)){
for (var_y in colnames(df_houses_prices)){
if (var_x != var_y){
print(df_houses_prices %>% select("X" = var_x, "Y" = var_y) %>%
ggplot(aes(x=X, y=Y)) +
geom_point(col = "blue") + ggtitle(paste0(var_y, " vs " ,var_x )) +
theme_minimal())
}
}
}## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(var_x)` instead of `var_x` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(var_y)` instead of `var_y` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 265 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 265 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 259 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_point).
Imputación de data faltante
Verificacion de datos faltantes
colNas <- colnames(df_houses_prices)[!complete.cases(t(df_houses_prices))]
colNas## [1] "LotFrontage" "FireplaceQu" "MasVnrArea"Porcentaje de NAS
incompleteData <- df_houses_prices %>% select(colNas)## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(colNas)` instead of `colNas` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.incompleteData## LotFrontage FireplaceQu MasVnrArea
## 1 65 <NA> 196
## 2 80 TA 0
## 3 68 TA 162
## 4 60 Gd 0
## 5 84 TA 350
## 6 85 <NA> 0
## 7 75 Gd 186
## 8 NA TA 240
## 9 51 TA 0
## 10 50 TA 0
## 11 70 <NA> 0
## 12 85 Gd 286
## 13 NA <NA> 0
## 14 91 Gd 306
## 15 NA Fa 212
## 16 51 <NA> 0
## 17 NA TA 180
## 18 72 <NA> 0
## 19 66 <NA> 0
## 20 70 <NA> 0
## 21 101 Gd 380
## 22 57 Gd 0
## 23 75 Gd 281
## 24 44 TA 0
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## 27 60 <NA> 0
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## 29 47 Gd 0
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## 36 108 Gd 132
## 37 112 <NA> 0
## 38 74 TA 650
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## 41 84 TA 101
## 42 115 Gd 0
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## 47 48 Ex 0
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## 1460 75 <NA> 0Calculo porcentaje NA
porcentajeNAS <- as.data.frame(apply(incompleteData, MARGIN = 2, function(col) mean(is.na(col))))
colnames(porcentajeNAS ) <- c("porcentaje")
porcentajeNAS## porcentaje
## LotFrontage 0.177397260
## FireplaceQu 0.472602740
## MasVnrArea 0.005479452procesables <- porcentajeNAS %>% filter(
porcentajeNAS < 0.05
)
procesables## porcentaje
## MasVnrArea 0.005479452